Amanda Hoover

Tech companies are pulling a classic layoff switcheroo

Tech bosses say AI will make us work less. So far, that’s mostly meant fewer of us will be working for them full-time.

Meta cut hundreds of workers last week. Oracle is reportedly considering thousands of layoffs to shore up money for its data center costs. Atlassian cut 10% of its workers this month as it restructured to focus on AI. Block laid off 4,000 employees in February, about 40% of the company. A report from career transition firm Challenger, Gray, and Christmas found that AI has been cited as the rationale behind some 92,000 job cuts at US-based companies since 2023, nearly a two-thirds of which came in 2025.

But the AI-driven layoffs aren’t the robo-job apocalypse they may seem.

It’s not that generative AI is sophisticated enough to absorb all of these desk jobs. It’s that tech companies are shifting investment in an attempt to win the gen AI race — which creates a tidy shorthand to justify a steady slimming of their payrolls. Many of these companies are then bringing people back or hiring new ones to similar roles, potentially finding cheaper ways to get the job done. A survey conducted by consulting firm Robert Half in late 2025 found that 29% of 2,000 hiring managers said they have reopened positions previously eliminated after implementing AI. Fifty-five percent said they planned to increase the number of contract or temporary workers within the first half of 2026, while 60% said the same for full-time workers. A recent report from advisory firm Gartner predicted that half of companies cutting customer service staff and ascribing the shift to AI will look to rehire people for similar roles by next year.

“Most layoffs right now aren’t actually happening due to AI,” says Kathy Ross, a senior director analyst at Gartner. “AI might have played a role, but they’re not a result necessarily of AI successes. Instead, the layoffs seem to be part of a broader strategy to reinvest funds in AI, hoping for success down the line.”

That big investment now can come at the expense of workers’ security. And it could drastically reshape the workforce and erode the little loyalty left between employees and their employers.


The number of workers clocking in for companies without actually working there as full-time employees has been on the rise for decades. A US Bureau of Labor Statistics report from 2001 estimated contingent workers accounted for 4.3% of the workforce in 1999. Today, some estimates with broader definitions of contingent workers put the proportion at 40% of US workers — MBO partners, a talent platform, estimates that 73 million people work as independents.

Contractors have been the fuel behind the tech industry’s growth almost since its inception. In the 1990s, Microsoft hired contractors and put them into “permatemp” roles, for years, creating a two-tiered system among employees. In 2000, the company settled a class action lawsuit for $97 million, after contract workers argued that they had been employed for too long to not receive the benefits offered to salaried workers. As of 2019, Google had more temporary workers than full-time employees, according to a New York Times report (Google did not respond to a request for comment about the current breakdown of its workforce). When companies like Uber, Amazon, and Meta expanded wildly in the 2010s, they turned to contract workers in the US and abroad to take on the often low-paying, arduous work of driving vehicles, delivering purchases, or moderating content. Recent research from freelance platform Upwork found that 77% of business leaders say the AI era is increasing their need to hire contract workers with specialized skills.

Most layoffs right now aren’t actually happening due to AI.Kathy Ross, a senior director analyst at Gartner

Those who held full-time, in-house jobs at tech companies were part of Silicon Valley’s golden era. They were offered generous parental leave, high salaries, stock options, and perks like free lunch and dinner. Now, as companies make deep cuts and offload some work to contractors, they’re changing the dynamic between employer and worker. Rob Lalka, a professor at Tulane University’s Freeman School of Business, says the move shifts Silicon Valley’s culture “towards a more ‘masculine energy,’ to use Zuckerberg’s phrase, that is more assertive and my way or the highway and dominant in a way that is now feeding into company culture.” The change is part of a long tech industry “attempt to minimize the number of people they have to have long-term relationships with through traditional employment,” says David Weil, an economics professor at Brandeis University. “It’s just part of this larger dance,” he says, amplified by AI, where “very profitable organizations want to share as little as they can with the people who create a lot of the value.”

That’s how one worker felt after he was laid off from his job at Microsoft several years ago. The worker, whose name has been omitted because he is now again employed at the company, says AI wasn’t explicitly mentioned as a reason for the elimination for his job, but the company’s intent to go all-in on AI was clear. The end of his full-time tenure meant a loss in unvested stock. Soon after, he tells me, a third-party contractor company reached out about working for the same team, as they sought to restaff with contract workers. This worker says he passed on the opportunity. After a yearlong job search, he got a full-time offer — back at Microsoft, but in a lower-ranked role that paid about a third less. He says he felt he “had little choice” but to take the job, which he still has today. “My morale has taken an enormous hit.” In 2025, Microsoft cut 15,000 jobs. The company declined to comment.

Companies have cut workers while keeping dozens of job posts open on their sites, or have moved swiftly to rehire workers. At Block, at least one worker still on staff said she was offered a retention package that increased pay by tens of thousands of dollars, and a handful of laid-off workers were brought back onto the job. Klarna CEO Sebastian Siemiatkowski has aggressively cut head count, halving the staff through layoffs, attrition, and an ongoing hiring freeze. The company uses an AI assistant for routine customer support; it’s also turning to contract workers to handle what the AI can’t. Siemiatkowski announced last year that Klarna is building “an Uber type of setup,” recruiting customers to work in a gig role to answer more difficult questions. “They can actually jump on and work for Klarna’s customer service,” he said on the podcast “20VC.” “These are our most passionate customers,” he said. “And now they earn extra money by actually working on our customer service.” Klarna did not respond to a question about the size of its customer service contract workforce.

The reality is that the companies are hiring more contractors and fewer full-time employees because it makes them more money.Maureen Wiley Clough, host of “It Gets Late Early”

The rosy picture of answering angry customer complaints for spare cash or taking on contract work to “be your own boss” doesn’t resonate with everyone. Contract workers often miss out on the benefits of a full-time gig, like health insurance, 401(k)s, unemployment insurance, stock options, and stability. They also have less recourse if they experience sexual harassment or discrimination. Some contract workers prefer having autonomy over their schedules — but a larger shift toward contract work could further divide who gets retirement contributions and healthcare. “They’re trying to pull the wool over our eyes by saying how great and how wonderful flexible employment is, how we can all be our own boss and how we can go from company to company and gain experience,” says Maureen Wiley Clough, who hosts the podcast “It Gets Late Early” about aging in the workforce. “The reality is that the companies are hiring more contractors and fewer full-time employees because it makes them more money.”


The era of massive AI investment is creating a system where some employees are glorified for their AI expertise and others wonder if they’ll be pushed out. This past summer, Meta was on a recruiting spree for the top AI talent, offering pay packages that reportedly amounted to hundreds of millions of dollars. My colleague Pranav Dixit and I reported that a winner-take-all era was emerging. Recent layoffs and a restructuring of Meta’s Reality Labs team to put workers into AI-native pods show the company is continuing its focus on AI-first and more nimble, small teams. A Meta spokesperson tells me that teams at Meta are regularly restructured to “ensure they’re in the best position to achieve their goals,” and that the company is searching for “other opportunities for employees whose positions may be impacted” in the latest layoffs.

Those who have jobs are keeping a tight hold on them, as across industries, people are struggling to secure jobs at all, let alone ones that help them advance their net worth and careers. As my colleague Aki Ito reported, pay cuts are in — 40% of white-collar workers who changed jobs at the end of 2025 took pay cuts of 10% or more, the highest proportion in a decade, according to research from Revelio Labs. The number of workers hopping jobs for raises of more than 10% simultaneously plummeted.

Rapid layoffs can backfire, says Kathy Ross. They can lead to damage to a company’s reputation, lost institutional knowledge, and disruptions in productivity as teams reconfigure. Those losses could be amplified by a lagging realization of AI-induced productivity: MIT published research last year finding that 95% of AI pilot programs had not led to increased productivity or savings, and research from the University of California, Berkeley, shows that AI is intensifying work rather than reducing the need for human labor. If tech companies continue to deteriorate the employee-employer relationship — either by the dizzying pace of layoffs or a pivot to contractors — they threaten to hamper the already weakening social contract between workers and companies.


Amanda Hoover is a senior correspondent at Business Insider covering the tech industry. She writes about the biggest tech companies and trends.

Business Insider’s Discourse stories provide perspectives on the day’s most pressing issues, informed by analysis, reporting, and expertise.




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Alice Tecotzky

Wells Fargo’s head of AI shares his playbook for staying in demand as banks weigh what the tech means for head count

Saul Van Beurden is the man helping Wells Fargo confront a question hanging over banks of every size: What happens to jobs in the age of AI?

He and his central team can’t, and shouldn’t, figure out what an AI-ready Wells Fargo looks like alone. The bank must teach employees skills to stay competitive in a changing industry, and they must choose to learn them, Van Beurden said.

“You cannot deny things,” Van Beurden, who is the head of AI and the co-CEO of consumer banking and lending, told Business Insider. “But how do you make it a thing where everybody has a role to play and takes their own accountability and responsibility?”

The bank is leaning on AI literacy programs and demos, among other things, to hopefully inspire “grassroots enthusiasm.” The goal is to make employees comfortable enough with the technology that they can be redeployed if their jobs change, or competitive in the job market if they leave Wells Fargo, he said. Wells Fargo doesn’t mandate AI usage, even as it bets the technology will help supercharge its growth following the Federal Reserve’s decision to lift a $1.95 trillion asset cap.

Van Beurden thinks that fluency starts outside the office. He’s trying to build an agent to help pull documents for his 2026 tax returns, and believes it’s crucial for employees to use AI in their personal lives, too.

“It’s really important to have that personal usage, to understand the power of what it can do. And then we are enabling that and allowing that to happen at the workplace,” he said.

Still, Van Beurden emphasized that everyone needs to “stay cognitive,” since AI could generate all of our ideas if we let it. He suspects that most college students are comfortable with technology but should invest time in activities like reading or playing chess. Staying sharp, he thinks, will help them in what’s broadly a brutal job market.

Wells’ workforce, like many of its competitors, is already changing because of AI. The bank’s CEO, Charlie Scharf, said in November that it will probably “have less head count as we look forward,” and added in December that generative AI has already made engineers up to 35% more productive.

Van Beurden didn’t say whether the bank would need 30% fewer engineers as a result or whether it would necessarily alter hiring, leaving it at, “it’s a great question.” Instead, he said that growth and head count aren’t always one-to-one.

“How great is it to grow without the need to hire people, because you have created the capacity to take on more clients, to take on more customers with the same amount of people?” he said, calling AI the “ideal tool” for that growth. Wells Fargo recorded $21.3 billion in revenue in the fourth quarter, up 4% year over year; revenue in its consumer bank, which Van Beurden oversees, rose 7% year over year.

The leaders of other big banks have also said that AI will likely eliminate some jobs and slow hiring, both publicly and in internal memos. JPMorgan CEO Jamie Dimon has said his bank has “huge redeployment plans.”

Efficiency promises and big technology budgets aside, the head count cuts haven’t yet materialized at most banks. Around 60% of 240 financial services CEOs surveyed by EY said they expect AI investments to maintain or boost their head count this year.




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I’m an Amazon tech lead who got promoted by building AI products. Here are my top vibe coding tips.

This as-told-to essay is based on a conversation with Anni Chen, who has worked in Amazon software engineering for about three-and-a-half years. It has been edited for length and clarity. Business Insider has verified her employment history.

AI helped me code, but more importantly, it helped with turning it into products. It’s the combination of grasping AI and translating it into scalable products that helped me get promoted faster.

I started off as a Software Engineer I, an entry-level role, in 2022. I was in the recommendations team working on serving recommendation widgets.

About two years ago, I started working on AI products on the side. That became huge and eventually spun off into its own team, which I’m a founding engineer of.

I was promoted in the recommendations team to Software Engineer II, and then I got promoted in the current team to senior engineer.

I focus on what we call memory, which powers personalization in generative AI experiences across Amazon.

AI writes 95% of my code

I started using AI as a side project to generate engaging titles for recommendation widgets when ChatGPT and Claude emerged. I saw how powerful it is in generating something really creative.

I started thinking: whenever I have a question or I want to code something up, I’ll just ask AI for help first before I attempt it.

I saw that the solution it came up with was leveling up my own code, and it helped me code more, too. Now I would say almost 95% of the code authored by me is written by AI.

I’m not just using AI to code; I also integrate AI’s output into products. I need to have a deep understanding of how AI works, what works well, and what doesn’t.

I have to be open and receptive to new models and tools coming out that can help with product iterations and make products better.

I work as a tech lead on large-scale LLM-driven systems in production environments, so I have a front-row seat to how AI-assisted workflows behave, not just in prototypes but under real-world scale and cross-team collaboration.

Top tips for vibe coding

The first tip is understanding the inner workings of LLMs and where they might fail.

LLMs are pre-trained — they’re trained on a large corpus, and it’s a probabilistic game. It’s followed by supervised fine-tuning, so the model will answer based on the structuring of a question and the answering format. Lastly, it’s followed by RLHF — reinforcement learning from human feedback.

By understanding these three steps, you can know, for example, when the LLM will not understand what you’re talking about, and when it needs domain knowledge from you. You will know when to use a new window or why hallucinations happen.

By understanding the limitations of the context window, you know when to break problems down. You will learn how to follow the structure to break things down into lower levels, and then you slowly focus on each component and generate.

By understanding the inner workings, you also know that you have to explain things to a peer. If you don’t explain in detail, it will default all those assumptions to the most common pattern, but that might not fit your use case.

My second tip: Think before vibe coding.

If you check the answer first, then your thoughts will be swayed by the answers. Compare your thoughts versus the LLM’s and see what the gaps are — what you didn’t know, and why the answer differs. From there, you know what implicit assumptions you haven’t told the LLM.

Thirdly, prompt for hard questions. Ask questions like what is the fallback when there is an error, or how this is going to scale? This is like a teacher asking a student, or a senior engineer asking a junior engineer to make sure the hard cases are covered. If you want the product to scale, think about it from day one and be conscious about asking those scaling questions.

Lastly, review and understand. Always review at each step, not just review after the whole code is generated. This ensures errors stop early rather than cascading all the way to the end, where you need to redo everything.

Creating wrong code is very dangerous. The presence of code makes people think, “Okay, this is good, it’s working.” But wrong code that enters production can cause more damage than the absence of functionality.

Understanding code is still important

You have to understand your own code. AI lowers the barrier to writing code, but not the responsibility for understanding it.

If something goes wrong and the code was committed by you, you’re the one responsible.

Imagine your code breaks in production, and you need to fix it, and you say, “I also don’t know, AI told me.” That’s not the correct way.

I don’t think we can entrust AI with such high-stakes tasks yet.

Understanding becomes easier with AI because it’s also a perfect learning opportunity. You can simply open another window and ask it to explain the concept.

If you ask in the same window about what it produced, it will explain only in that context. But you want to understand the concept more generally and see whether it makes sense to apply in this case.

Do you have a story to share about coding with AI? Contact this reporter at cmlee@businessinsider.com.




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